Using Mendelian randomization to unveil the truth: does COVID-19 impact QT interval prolongation?

doi:10.21203/rs.3.rs-4302501/v1

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Using Mendelian randomization to unveil the truth: does COVID-19 impact QT interval prolongation?

https://doi.org/10.21203/rs.3.rs-4302501/v1

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Background

QT interval, a vital marker of ventricular electrical activity during depolarization and repolarization, garnered significant attention during the COVID-19 pandemic. However, it remains unclear whether COVID-19 directly affects QT interval prolongation. This study leverages Mendelian randomization (MR) to investigate the genetic causation between COVID-19 and QT interval alterations.

Methods

In over 1000,000 individuals of European ancestry, genetic proxies representing three COVID-19 phenotypes (COVID-19, hospitalized COVID-19, and severe COVID-19), were identified under the primary MR assumption, and serve as instrumental variables (IVs). Genetic causal effects of COVID-19 on QT intervals from 84,630 UK Biobank participants were inferred using univariate two-sample MR (TSMR) and multi-exposure-adjusted multivariate MR (MVMR). MR-RAPS method and radial MR frame were used to provide robustness and outlier variant detection for effect assessment, and sensitivity analysis was further applied to detect the presence of horizontal pleiotropy.

Results

Independent 15, 33, and 29 IVs were used in COVID-19, hospitalized COVID-19, and severe COVID-19, respectively. Univariate TSMR analyses showed non-significant causal effect estimates between COVID-19 and the QT interval across all COVID-19 phenotypes. MR-RAPS and outlier-corrected radial MR analyses further supported this null causal estimation. In confounder-adjusted MVMR analysis, this nonsignificant causality was independent of BMI, smoking, and alcohol consumption. Sensitivity analyses failed to reveal any evidence of bias arising from horizontal pleiotropy, abnormal data distribution, or weak instruments.

Conclusions

Genetically, COVID-19 is not causally associated with QT interval prolongation. Inconsistent findings in observational research may be attributed to residual confounding.

COVID-19

QT interval

Mendelian randomization (MR)

causality

prolongation

Cardiovascular diseases continue to be a significant global cause of mortality, and the COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has exacerbated this burden^1-3. COVID-19 exerts a multi-organ, multi-level impact on the human body, with clinical manifestations ranging from asymptomatic cases to severe respiratory complications and fatal outcomes. Notably, the virus extends its influence beyond the respiratory system, resulting in a spectrum of cardiovascular complications, including a pronounced association with various cardiac arrhythmias, which has led to an increase in hospitalization rates and mortality^2-6.

Among the array of cardiovascular concerns arising from COVID-19, the regulation of the QT interval on electrocardiogram (ECG) has garnered substantial attention^7-15. QT interval, a vital parameter measured on ECGs, reflects the time required for ventricular depolarization and repolarization. It is influenced by a multitude of factors, including heart rate, age, gender, medications, electrolytes, and hormones¹⁶. Prolonged or shortened QT intervals are associated with an increased risk of arrhythmias and sudden cardiac death, with QT interval prolongation specifically linked to a heightened risk of ventricular tachycardia, particularly Torsades de Pointes (TdP), and the potential for fatal arrhythmias^17,18.

ECG serves as an essential tool in managing COVID-19 patients, unveiling cardiac involvement such as atrial fibrillation, prolonged QT intervals, and ventricular and supraventricular arrhythmias¹⁹. Observational studies have indicated an independent association between QT interval prolongation and COVID-19, suggesting that QT interval prolongation may serve as an early prognostic marker for disease progression and mortality^{4,8,13-15,20-22}.

While a growing body of research has identified genetic loci associated with COVID-19 susceptibility and severity, the precise relationship between these genetic markers and adverse outcomes in COVID-19 patients remains less clear^6,23-25. From a biological standpoint, although QT interval prolongation may be associated with SARS-CoV-2-induced myocarditis and changes in physiological parameters such as electrolytes and hormones^7,26, the observational and cross-sectional nature of these studies, coupled with the complexity of this relationship influenced by multiple factors, leaves us uncertain as to whether a genuine causal association exists between SARS-CoV-2 infection and QT interval prolongation.

To investigate whether COVID-19 causally induces changes in the QT interval, we conducted a Mendelian randomization (MR) analysis. MR analysis is a statistical methodology rooted in genetic epidemiology, leveraging genetic variations as instrumental to infer causal relationships between exposures and outcomes, mitigating the confounding and reverse causality biases commonly encountered in observational studies²⁷. In this context, we employed genetic variants associated with COVID-19 exposure as instrumental, employing both univariable MR (UVMR) and multivariable MR (MVMR) analyses to assess their impact on QT intervals, emphasizing the importance of establishing causation rather than mere correlation.

Study design

This study investigated the causal association between three distinct COVID-19 phenotypes as exposures and the QT interval outcome. The different COVID-19 phenotypes as binary variables included COVID-19 infection (COVID-19 vs. general population), hospitalized COVID-19 (hospitalized COVID-19 vs. general population), and severe COVID-19 (severe COVID-19 with respiratory failure vs. general population). Based on genome-wide association studies (GWAS), significant genetic variants (single nucleotide polymorphisms, SNPs) associated with the exposure were identified and served as genetic instruments. Genetic risk effects between SNP-exposure and SNP-outcome were estimated using univariable two-sample MR (TSMR), with potential confounders (BMI, smoking, and drinking as covariates) being accounted for in the inference through MVMR. Sensitivity analyses were conducted to further validate the robustness of the causal assessment results.

GWAS summary statistics for COVID-19, BMI, alcohol, and smoking

Summary statistics of three COVID-19 phenotypes were derived from publicly available GWAS meta-analysis outcomes within the COVID-19 Host Genetics Initiative database (https://www.covid19hg.org)²⁸. The datasets, including COVID-19 infection, hospitalized COVID-19, and severe COVID-19 phenotypes, were ethically accredited and underwent comprehensive adjustment for demographic factors²⁸. For the European population, the analysis included 122,616 cases and 2,475,240 controls in the COVID-19 infection phenotype, 32,519 cases and 2,062,805 controls in the hospitalized COVID-19 phenotype, and 13,769 cases and 1,072,442 controls in the severe COVID-19 phenotype. Additional GWAS summary statistics for BMI, alcohol consumption, and smoking were sourced from the IEU Open GWAS Project (http://gwas.mrcieu.ac.uk). The BMI dataset (GWAS ID: ieu-b-40) comprises data from 681,275 European samples, while alcohol (GWAS ID: ukb-b-5779) and smoking (GWAS ID: ebi-a-GCST90029014) phenotypes include sample data from 462,346 and 468,170 individuals, respectively.

GWAS summary statistics for QT interval

The summary statistics for the QT interval on ECG were derived from recent large-scale GWASs^29,30. Nauffal et al. analyzed the ECG records of 84,630 participants from the UK Biobank (comprising 93.5% of European ancestry)²⁹. The QT interval was extracted from 12-lead or 3-lead ECGs and corrected for heart rate using the Bazett formula, defined as QTc = ²⁹. Data of low quality and potential confounding factors influencing QT intervals were excluded from the study, and further details regarding data quality control can be found in the original publication²⁹. GWAS summary data for the study are accessible on the Cardiovascular Disease Knowledge Portal (CVDKP). SNPs serving as genetic instruments for the exposure were matched to this GWAS outcome data for the primary MR analysis.

Additionally, Hoffmann et al. incorporated QT interval measurements from 86,165 individuals (73.3% European ancestry) in the GWAS analysis (study accession number: GCST90165291, accessible through the NHGRI-EBI Human GWAS Catalog website: https://www.ebi.ac.uk/gwas/)³⁰. This QT interval outcome data (QT_Hof) was employed for replication analysis to verify the consistency of the primary MR outcomes.

Identification of genetic instruments

SNPs with genome-wide significance (P < 5 × 10^-8) in the GWAS for each COVID-19 exposure were designated as genetic instrumental variables (IVs). These IVs were strongly associated with COVID-19, not directly associated with the QT interval, and independent from any potential confounders, making them suitable for causal inference (the underlying assumption of the MR study). PhenoScanner excluded IVs directly related to possible confounders (especially associated with QT intervals) such as BMI, hypertension, diabetes, smoking, alcohol consumption, cholesterol, mental disorders, and insomnia³¹. Linkage disequilibrium (LD) clumping (r² = 0.001 and kb = 10000) on SNP data was performed to ensure the independence of their inclusion as IVs. The F statistic was employed to determine the included IVs' association power to eliminate the weak instruments' bias. LocusZoom (http://csg.sph.umich.edu/locuszoom/) is used for creating regional association plots of different lead SNPs (IVs with minimal P-values) using the combined hg19/1000 Genomes EUR reference panel.

UVMR and MVMR analysis

The MRC Integrative Epidemiology Unit (MRCIEU) TwoSampleMR software package was applied to harmonize the direction of effect between exposure and outcome SNPs and to perform MR analysis³². Pooled exposure-outcome causal effects determined by UVMR were calculated using MR-Egger, weighted median, and inverse variance weighted (IVW) (multiplicative random effects) methods. Other robust corrected scoring methods include MR-robust adjusted profile score (MR-RAPS) and penalized weighted median MR is further used for the estimation of causal effects. In addition, a radial MR framework was applied, which avoids data re-orientation and employs radial plots to better discern correlations and causal relationships³³.

The MVMR analysis directly estimated the specific associations between each exposure and the respective outcomes and potentially reflected the pleiotropic effects of the included IVs, effectively mitigating the interference of confounding factors on observed results. This analysis was achieved using the MR-PRESSO package. To facilitate clinical interpretation, the effect size of exposure on outcome was interpreted as the magnitude, in milliseconds, by which the presence or absence of the disease (COVID-19) altered the duration of the QT interval on ECG. For UVMR estimation, a P-value < 0.05 was considered indicative of a potential causal relationship. A Bonferroni multiple correction threshold of P < 0.01 (0.05/4) was deemed statistically significant in MVMR.

Sensitivity analyses

Sensitivity analyses were conducted to evaluate the potential influence of these genetic variants on causal estimations, considering the possibility of pleiotropic effects when utilizing a substantial collection of variants as IVs. In both the IVW and MR-Egger analyses, heterogeneity for IVs was assessed using Cochran's Q-statistic. Leave-one-out sensitivity was performed to exclude the possibility of one SNP having an outsized effect on the overall results. Additionally, the Egger intercept test was employed to investigate the presence of horizontal pleiotropy, and when the P-value > 0.05, it indicated the absence of pleiotropy. MR-RAPS, which proposes a consistent and asymptotically normal estimator by adjusting the profile score, was utilized to aid in interpreting potential pleiotropic effects and enhance the accuracy of causal effect estimations³⁴. Furthermore, in IVW or MR-Egger analysis, radial MR improves the detection of outliers and influential data points, allowing us to perform outlier-corrected MR assessments³³.

Genetic IVs for MR analysis

Following meticulously applying stringent criteria to ensure relevance and independence, we identified sets of SNPs from the COVID-19, hospitalized COVID-19, and severe COVID-19 GWAS cohorts, totaling 15, 33, and 29 variables, respectively (Table S1 to Table S3). Regional association plots were created to visualize the chromosomal positions of the lead SNPs for each exposure (Figure S1). These SNPs, as IVs for exposure, were compatible and harmonized with the outcome GWAS dataset (including QT_(Hof) as a duplicate analysis) for further MR assessment. The IVs were excluded as weak instrumental bias based on F-statistics ranging from 27.04 to 845.47 (Table S1 to Table S3).

Causal estimation of genetically determined COVID-19 on QT interval

Univariate TSMR was initially employed to assess the genetic causality of COVID-19 on QT interval. The IVW method for genetic prediction revealed no significant correlation between the three COVID-19 phenotypes and QT interval changes [COVID-19: βIVW (95% CI): -0.44 (-1.72, 0.84), P = 0.50; hospitalized COVID-19: βIVW: 0.12 (-0.57, 0.80), P = 0.74; severe COVID-19: βIVW: 0.11 (-0.29, 0.51), P = 0.58] (Figure 1 and Figure S2A). Consistent causal conclusions were obtained in supplementary analyses, including MR-Egger and weighted median (Figure 1 and Figure S2A). The MR-RAPS score further confirmed the absence of a causal association [COVID-19: βMR-RAPS (95% CI): -0.44 (-1.67, 0.79), P = 0.48; hospitalized COVID-19: βMR-RAPS: 0.12 (-0.35, 0.59), P = 0.62; severe COVID-19: βMR-RAPS: 0.11 (-0.22, 0.44), P = 0.50] (Figure 1).

Null causal estimates were similarly observed in penalized weighted median MR and in both IVW and MR-Egger analyses using a radial MR framework [COVID-19: βradial IVW (95% CI): -0.44 (-1.71, 0.83), P = 0.50; hospitalized COVID-19: βradial IVW: 0.12 (-0.56, 0.80), P = 0.74; severe COVID-19: βradial IVW: 0.11 (-0.29, 0.51), P = 0.58] (Figure 2). Radial MR plots identified outliers for each phenotype (Table 1 and Figure 3A). Notably, corrective analysis post-outlier removal did not alter the initial MR inference (Figure 2).

The direct causal effect of COVID-19 on QT interval adjusted by BMI, alcohol, and smoking

To limit the potential pleiotropy, MVMR with BMI, alcohol, and smoking as covariates was also performed. Consistent with the findings of the UVMR, no significant causal effect was found between the multi-exposure-adjusted COVID-19 infection phenotype and prolonged QT interval (β_{BMI+Alcohol+Smoking}(95% CI): -0.77 (-2.44, 0.91), P = 0.37) (Figure 4). Similarly, the adjustment for smoking and alcohol consumption concerning two other COVID-19 phenotypes did not yield positive MR results [hospitalized COVID-19: β_Alcohol (95% CI): -0.38 (-0.81, 0.06), P = 0.09, β_Smoking: -0.25 (-0.64, 0.13), P = 0.20; severe COVID-19: β_Alcohol: -0.21 (-0.53, 0.11), P = 0.20, β_Smoking: -0.13 (-0.44, 0.18), P = 0.40] (Figure 4).

Notably, in contrast to the null causal estimates observed in the UVMR analysis, the BMI-adjusted hospitalized COVID-19 and severe COVID-19 phenotypes exhibited a nominally significant shortening of the QT interval [hospitalized COVID-19: β_BMI (95% CI): -0.45 (-0.82, 0.08), P = 0.02; severe COVID-19: β_BMI: -0.32 (-0.60, -0.03), P = 0.03] (Figure 4). However, this significance did not reach the defined threshold for Bonferroni multiple corrections. Consequently, interpreting BMI as either existing within the pleiotropic pathway of genetic variation affecting the QT interval, or as a mediator of causality from genetic variation through COVID-19 to the QT interval should be approached with caution.

Validated causal estimates of COVID-19 on QT_(Hof) interval

To corroborate the consistency with the primary findings, additional QT interval GWAS data-QT(_Hof) was employed for replication analysis. The causality estimates between genetically predicted exposures and outcomes in this analysis broadly recapitulated the primary study's findings (Figure S2B and Figure S3). For the COVID-19 infection phenotype, the IVW and MR-RAPS methods suggested a potential association with QT interval prolongation [COVID-19: β_IVW (95% CI): -1.68 (0.38, 2.99), P = 0.01] (Figure S3). However, other evaluation models did not support this conclusion, implying the possibility of false positives (Figure S3). Moreover, the radial MR approach identified potential outliers in the analysis of the three cohorts, with 3, 5, and 3 outliers, respectively (Table 1 and Figure S4A). Outlier-corrected radial MR did not reveal significant causal estimates in any of the COVID-19 cohorts (Figure S3), underscoring the robustness of the primary MR analysis results.

Sensitivity analysis

Sensitivity analyses were performed to evaluate potential bias in the MR analysis results. Heterogeneity assessment using Cochran's Q test showed substantial heterogeneity for selected IVs for almost all COVID-19 cohorts on QT intervals (P < 0.05) (Table 2). No significant departure from zero for the intercept of the MR Egger method (P > 0.05) indicated no evidence of bias due to pleiotropy (Table 2). Furthermore, the Q-Q plot for MR-RAPS demonstrated that the analysis adhered to the normality assumption of the MR model (P > 0.05), suggesting that the estimates converge to the true causal effect (Figure 3B and Figure S4B). Leave-one-out analysis revealed that none of the individual SNPs independently drove the overall MR effect (Figure S5).

Numerous observational studies and case reports have suggested a potential link between COVID-19 and the prolongation of the QT interval on ECG^{4,8,13-15,20-22,35,36}. To address the limitations of confounding factors and reverse causality inherent in observational research²⁷, we conducted an MR analysis to investigate whether COVID-19 has a genetic causal impact on QT interval changes. In summary, our findings did not support a causal effect of COVID-19 on longer or shorter QT intervals. These conclusions are derived from large GWAS sample datasets, multiple robust MR assessments, and confirmatory analyses. Nevertheless, considering the differences between our MR results and observational findings, we contemplate two possible explanations. These disparities may, in part, stem from the limitations of observational studies, including challenges related to confounding factors and reverse causality. Additionally, measurements of the QT interval on ECG may be influenced by various factors, including medication usage and individual physiological conditions.

In addition to specific single-gene inherited mutations that cause congenital long QT syndromes, acquired factors affecting QT interval duration are complex^37,38. The QT interval reflects the contribution of multiple ion currents, including potassium and calcium ion channels, to the cardiac action potentials. Ion channels are essential for various cellular functions and have been implicated as key host factors in many viral infections^4,14. A recently published GWAS analysis of genetic variants in patients with severe COVID-19 identified 49 genomic loci associated with disease susceptibility or severity, implicating processes including inflammatory signaling, immune metabolism, and blood coagulation²⁵. These findings provide novel insights into COVID-19-mediated multisystem complications. However, the complex biological pathways underlying COVID-19-induced arrhythmias, specifically QT interval prolongation, remain poorly understood. Emerging evidence suggests that SARS-CoV-2 genes encode K⁺ channels and may interfere with the regulation of Ca²⁺ handling in cardiomyocytes, leading to impaired cardiac contractility and increased risk of arrhythmias¹⁵. An MR analysis supports the indication of a causal effect between serum calcium levels and QT interval prolongation from the UKB³⁹, whereas it is not known whether the relationship between COVID-19 and QT interval is mediated through changes in serum calcium concentration or other potential mediators affected by COVID-19. Our evidence suggests that COVID-19 does not directly influence QT interval changes, and, based on current understanding, much work remains to decipher which biomolecules or indicators play a mediating role in their correlation.

During a pandemic, multiple factors such as myocarditis, electrolyte disturbances, hormonal changes, and autonomic nervous system dysfunction can influence QT interval^4,7,40,41. Mechanistically, excessive inflammation may alter the expression and function of several ion channels, especially K⁺ and Ca²⁺ channels, resulting in inflammatory cardiac channelopathies, QT prolongation, and arrhythmias^11,42. Therefore, SARS-CoV-2 infection is associated with myocarditis and alterations in internal physiological factors⁷. Our MR analysis employing genetic variants of COVID-19 as the IVs may not fully account for these complex processes. Additionally, the multifaceted nature of COVID-19-induced inflammatory responses and multi-organ complications poses challenges for elucidating the pathogenic mechanisms of potential complications, including QT interval prolongation^1,5,11. Furthermore, the association between COVID-19 and QT interval prolongation may involve undiscovered genetic or environmental factors. Notably, certain existing drugs, such as chloroquine and lopinavir, have been widely discussed for their potential to cause QT prolongation when used in COVID-19 treatment^43-50. These drugs, however, had previously been reported to cause QT prolongation by targeting the K_V11.1 potassium channel even before the emergence of COVID-19 and untreated COVID-19 patients also exhibited QT interval prolongation^4,14,43,51. The MR analysis results are independent of the genetic variants influenced by drug effects in COVID-19. Further research on the causal relationship between specific medications and QT interval prolongation can help explain how drugs may affect QT intervals during COVID-19 treatment.

Consideration of the temporal relationship between exposure and outcome is of paramount importance. COVID-19 is a complex and dynamic disease, and the timing of ECG measurements concerning the infection process may hold significant implications¹². The impact of COVID-19 on QT intervals might vary with different stages of the disease, and our MR analysis did not adequately capture this temporal factor. To comprehensively understand the intricate relationship between COVID-19 and QT intervals, more datasets covering diverse population cohorts and different disease stages will provide deeper insights into the underlying genetic factors and potential mechanisms governing the association.

Furthermore, although our study did not find a genetic causal relationship between COVID-19 and QT interval prolongation, it does not rule out the possibility that COVID-19 infection may increase the risk of QT interval prolongation or arrhythmias. GWAS summary statistics for COVID-19 and QT interval duration are continually evolving to identify novel variants. For example, in the GWAS summary of QT intervals we included, despite meticulous consideration of polygenic risk-equivalent variants and single-gene putatively pathogenic rare variants, over 75% of individuals with markedly prolonged QT (>480 ms) did not exhibit these genetic variants²⁹. This suggests that our understanding of the genetic factors influencing QT duration is limited and underscores the substantial influence of non-genetic factors on QT intervals.

To the best of our knowledge, this study represents the inaugural application of MR to scrutinize the causal association between COVID-19 and the QT interval. The research utilized the most extensive available genome-wide association data for exposure and outcome, employing both fundamental and advanced MR methods for effect estimation. Multiple sensitivity analysis approaches were employed to confirm the robustness of the findings, furnishing largely unbiased causal estimates. Nevertheless, the study is not without limitations. One notable constraint is the absence of data from non-European populations, thereby constraining the generalizability of the conclusions. Moreover, the possibility that unmeasured variables, impacting both COVID-19 and the QT interval, remains unaccounted for, necessitating further exploration to enhance our understanding of the underlying mechanistic relationships. Hence, it is essential to acknowledge that our findings warrant replication and validation in alternative datasets and diverse populations. In summary, forthcoming investigations should capitalize on larger, more diverse, and comprehensive datasets to account for increased variance, enabling a more robust genetic inference of the link between COVID-19 infection and QT intervals. Such endeavors will offer supplementary evidence for the prevention and management of cardiac complications associated with COVID-19.

Our MR analysis did not support a causal relationship between COVID-19 and QT interval. Incongruent findings between observational studies may be attributed to the residual confounding. More extensive investigations using comprehensive and diverse datasets are warranted to validate these findings and delve into potential underlying mechanisms.

Ethics approval and consent to participate

This work is a reanalysis of publicly available data, ethical approval was granted in all original studies, and again ethical approval is not required.

Consent for publication

All authors approved the final version and agreed to be responsible for the study.

Availability of data and material

All data supporting the findings of this study are included in this article.

Competing interests

None.

Funding

This work was supported by the Special Major Application Research Project for COVID-19 Prevention and Control in Universities, Department of Education of Guangdong, Provincial Program of Innovation and Strengthening School, Guangdong, China [grant number: 2020KZDZX1093].

Author contributions

ZZ: Conceptualization; Data interpretation; Writing-Original draft preparation; Software; Resources.

XT: Conceptualization; Writing-review & editing; Funding acquisition.

YS: Methodology, Investigation; Supervision.

Acknowledgments

We appreciate the contributions of participants and investigators from the COVID-19 Host Genetics Initiative, UK Biobank, Cardiovascular Disease Knowledge Portal (CVDKP), and NHGRI-EBI Human GWAS Catalog for sharing their data publicly. Special thanks to the IEU Open GWAS program for providing accessible summary data. Their collaboration was critical to the success of this study and the advancement of scientific understanding.

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Table 1: Outliers in MR analysis identified by radial MR methods

Exposure	Outcome	Outlier(s)
COVID-19	QT interval	rs11264339
Hospitalized COVID-19		rs1634761, rs2102497, rs2897075, rs492602, rs63750417, rs78295726
Severe COVID-19		rs2897075, rs368565, rs62056905
COVID-19	QT interval (Hof)	rs11264339, rs184781326, rs679574
Hospitalized COVID-19		rs63750417, rs492602, rs2326562, rs17412601, rs1634761
Severe COVID-19		rs62056905, rs368565, rs2236645

Table 2: MR sensitivity analysis results

Exposure	Outcome	Heterogeneity (Cochrane 's Q test)	Pleiotropy （MR Egger）		Normality (MR-RAPS)
		Heterogeneity (Cochrane 's Q test)	Pleiotropy （MR Egger）		Normality (MR-RAPS)
		P-value	Intercept	P-value	W statistics	P-value
COVID-19	QT interval	0.37	-0.05	0.48	0.94	0.40
Hospitalized COVID-19		5.77E-04	-0.02	0.82	0.98	0.89
Severe COVID-19		0.05	0.00	0.98	0.95	0.18
COVID-19	QT interval (Hof)	0.01	0.06	0.37	0.94	0.37
Hospitalized COVID-19		1.95E-05	-0.04	0.52	0.97	0.49
Severe COVID-19		0.01	-0.05	0.28	0.95	0.23

No competing interests reported.

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Using Mendelian randomization to unveil the truth: does COVID-19 impact QT interval prolongation?

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study design

GWAS summary statistics for COVID-19, BMI, alcohol, and smoking

GWAS summary statistics for QT interval

Identification of genetic instruments

UVMR and MVMR analysis

Sensitivity analyses

Result

Genetic IVs for MR analysis

Causal estimation of genetically determined COVID-19 on QT interval

The direct causal effect of COVID-19 on QT interval adjusted by BMI, alcohol, and smoking

Validated causal estimates of COVID-19 on QT_(Hof) interval

Sensitivity analysis

Discussion

Conclusion

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and material

Competing interests

Funding

Author contributions

Acknowledgments

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1

Using Mendelian randomization to unveil the truth: does COVID-19 impact QT interval prolongation?

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study design

GWAS summary statistics for COVID-19, BMI, alcohol, and smoking

GWAS summary statistics for QT interval

Identification of genetic instruments

UVMR and MVMR analysis

Sensitivity analyses

Result

Genetic IVs for MR analysis

Causal estimation of genetically determined COVID-19 on QT interval

The direct causal effect of COVID-19 on QT interval adjusted by BMI, alcohol, and smoking

Validated causal estimates of COVID-19 on QT(Hof) interval

Sensitivity analysis

Discussion

Conclusion

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and material

Competing interests

Funding

Author contributions

Acknowledgments

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1

Validated causal estimates of COVID-19 on QT_(Hof) interval